A SINGLE NEAREST NEIGHBOR FUZZY APPROACH FOR PATTERN RECOGNITION

Abstract

The main aim of this paper is to introduce the single nearest neighbor approach for pattern recognition and the concept of incremental learning of a fuzzy classifier where decision making is based on data available up to time t rather than what may be available at the start of the trial, i.e. at t = 0. The single nearest neighbor method is explained in the context of solving the classic two-spiral benchmark. The proposed approach is further tested on the electronic nose coffee data to judge its performance on a real problem. This paper illustrates: (1) a novel fuzzy classifier system based on the single nearest neighbor method, (2) its application to the spiral benchmark taking the incremental pattern recognition approach, and (3) results obtained when solving the two-spiral problem with both nonincremental and incremental methods and coffee classification with the nonincremental method. The results show that incremental learning leads to improved recognition performance for spiral data and it is possible to study the behavioral characteristics of the classifier with possibility related parameters.